CN107370664A - A kind of effective microblogging junk user finds system - Google Patents
A kind of effective microblogging junk user finds system Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
- H04L51/52—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail for supporting social networking services
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- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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Abstract
The invention provides a kind of effective microblogging junk user to find system, including modeling module, user's sort module and junk user determining module, the modeling module is used to establish microblog users network model, user's sort module is classified based on microblog users network model to microblog users, and the junk user determining module determines the junk user in microblog users based on user's classification；The microblog users network model is established based on user's concern relation, is specially：It regard the digraph H=(W, B) that user's concern relation in microblogging is formed as microblog users network model, wherein, W is microblog users set, and B is side collection, if user has concern relation, side be present between user.Beneficial effects of the present invention are：Microblog users network is modeled based on the concern relation of microblog users, can effectively find the junk user in microblogging.
Description
Technical field
The present invention relates to technical field of microblog, and in particular to a kind of effective microblogging junk user finds system.
Background technology
Microblogging is quick in netizen by characteristics such as opening, termination extension, content terseness and the low thresholds of platform
Infiltration, develops into an important social media, and it not only turns into netizen and obtains news and current affairs, human communication, self table
Reach, society shares and the important medium of communal participation, also as social public opinion, brand names and product promotion, tradition
The Important Platform of broadcasting media.However, with microblogging service popularization with it is fast-developing, exist largely with spy privacy information,
Business marketing, raise user's popularity, manufacture and link with propagating the artificial rubbish for the purpose of public opinion etc. and junk user.How to send out
Existing junk user, createing good network environment turns into problem of the pendulum in face of people.
The content of the invention
In view of the above-mentioned problems, the present invention is intended to provide a kind of effective microblogging junk user finds system.
The purpose of the present invention is realized using following technical scheme：
Provide a kind of effective microblogging junk user and find system, including modeling module, user's sort module and rubbish
User's determining module, the modeling module are used to establish microblog users network model, and user's sort module is used based on microblogging
Family network model is classified to microblog users, and the junk user determining module is classified based on user to be determined in microblog users
Junk user；The microblog users network model is established based on user's concern relation, is specially：By user's concern relation in microblogging
The digraph H=(W, B) of formation is used as microblog users network model, wherein, W is microblog users set, and B is side collection, if with
There is concern relation in family, then side be present between user.
Beneficial effects of the present invention are：Microblog users network is modeled based on the concern relation of microblog users, can
Effectively find the junk user in microblogging.
Brief description of the drawings
Using accompanying drawing, the invention will be further described, but the embodiment in accompanying drawing does not form any limit to the present invention
System, for one of ordinary skill in the art, on the premise of not paying creative work, can also be obtained according to the following drawings
Other accompanying drawings.
Fig. 1 is the structural representation of the present invention；
Fig. 2 is user's sort module structural representation of the present invention.
Reference：
Embodiment
The invention will be further described with the following Examples.
Referring to Fig. 1, Fig. 2, a kind of effective microblogging junk user of the present embodiment finds system, including modeling module 1, use
Family sort module 2 and junk user determining module 3, the modeling module 1 are used to establish microblog users network model, the user
Sort module 2 is classified based on microblog users network model to microblog users, and the junk user determining module 3 is based on user
Classification determines the junk user in microblog users；The microblog users network model is established based on user's concern relation, is specially：
It regard the digraph H=(W, B) that user's concern relation in microblogging is formed as microblog users network model, wherein, W is microblog users
Set, B is side collection, if user has concern relation, side be present between user.
The present embodiment is modeled based on the concern relation of microblog users to microblog users network, can effectively be found micro-
Junk user in rich.
Preferably, user's sort module 2 includes characteristic of division determining unit and taxon, and the characteristic of division is true
Order member is used to determine the feature for classifying, and the taxon is used to determine class of subscriber according to characteristic of division；Described point
Category feature determining unit includes junk user characteristic of division determination subelement and normal users characteristic of division determination subelement, described
Junk user characteristic of division determination subelement is used to determine the feature for judging junk user, and the normal users characteristic of division determines
Subelement is used to determine the feature for judging normal users.
The feature for judging junk user includes the first junk user feature F1With the second junk user feature F2：
The first junk user feature determines in the following ways：Calculate the first junk user characteristic index of user：
In formula, p (xi) represent the time series of user being divided into m subsequence, xthiIndividual subsequence is published the news number
The ratio of total time sequence is accounted for, if wl1≥T1, then the user meet the first junk user feature, T1For given threshold；
The second junk user feature determines in the following ways：Calculate the second junk user characteristic index of user：
In formula, d1Represent the message number that user includes "@" in publishing the news, l1Represent that user includes in publishing the news
The message number of " http//", d represent the message sum that user delivers；If wl2≥T2, then it represents that user meets the second rubbish
User characteristics, T2For given threshold.
The feature for judging normal users includes the first normal users feature C1With the second normal users feature C2：
The first normal users feature determines in the following ways：Calculate the first normal users characteristic index of user：
If wz1≤T3, then the user meet the first normal users feature, T3For given threshold；
The second normal users feature determines in the following ways：Calculate the second normal users characteristic index of user：
If wz2≤T4, then it represents that user meets the second normal users feature, T4For given threshold.
This preferred embodiment Overall Acquisition user's various features, is used to be follow-up by establishing a variety of classification judging characteristics
Family classification is laid a good foundation, specifically, the first junk user characteristic index and the first normal users characteristic index reflect user
Rule of posting, the second junk user characteristic index closes the second normal users characteristic index and reflects the transmission junk information of user
Situation.
Preferably, the taxon determines class of subscriber in the following ways：
(1) for any user w ∈ W, the characteristic set F={ F for judging junk user are giveni, i=1,2, if met
The ith feature of junk user, then it can be high as the probability of junk user, if only existing a feature so that user w has
Higher probability is junk user, then the user is doubtful junk user, if there is two features so that user w has higher
Probability be junk user, then the user is approximate junk user；
(2) for any user w ∈ W, the characteristic set C={ C for judging normal users are givenj, j=1,2, if met
J-th of feature of normal users, then it can be high as the probability of normal users, if only existing a feature so that user w has
Higher probability is normal users, then the user is doubtful normal users, if there is two features so that user w has higher
Probability be normal users, then the user is approximate normal users；
(3) for any user w ∈ W, if being both unsatisfactory for junk user judges feature, also it is unsatisfactory for normal users judgement
Feature, then user w is uncertain user.
This preferred embodiment is realized by determining that the feature of junk user and the feature of normal users determine class of subscriber
The Accurate classification of user, specifically, the quantitative determination user by meeting feature belong to the general of junk user and normal users
Rate, determine that junk user is laid a good foundation to be follow-up.
Preferably, the junk user determining module 3 determines junk user in the following ways：
(1) user's score DF is calculated：
In formula, a1Represent the number that user is paid close attention to by doubtful normal users, a2Represent what user was paid close attention to by approximate normal users
Number, b1Represent that user pays close attention to the number of doubtful junk user, b2Represent that user pays close attention to the number of approximate junk user；
(2) if user is approximate junk user and meets user's score DF>0.2, if user is doubtful junk user and expired
Sufficient user's score DF>0.5, if user is uncertain user and meets user's score DF>1, if user be doubtful normal users and
Meet user's score DF>2, if user is approximate normal users and meets user's score DF>4, then user is defined as rubbish and used
Family, it is otherwise normal users.
Statistical nature judgement is only relied on, the unconspicuous junk user of Partial Feature may be missed, this preferred embodiment uses
User's score determines junk user with the mode that feature is combined, and reduces the False Rate of junk user discovery, improves rubbish
The discovery accuracy rate of user.
Find that system excavates junk user, the junk user number point of excavation using effective microblogging junk user of the invention
Not Wei 10,20,30,40,50 when, accuracy rate, which counts, to be found to junk user discovery time and junk user, compared with rubbish
Rubbish user has found that system is compared, caused to have the beneficial effect that shown in table：
| Junk user number | Junk user discovery time shortens | Junk user finds that accuracy rate improves |
| 10 | 23% | 21% |
| 20 | 25% | 20% |
| 30 | 24% | 25% |
| 40 | 26% | 22% |
| 50 | 24% | 23% |
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of scope is protected, although being explained with reference to preferred embodiment to the present invention, one of ordinary skill in the art should
Work as understanding, technical scheme can be modified or equivalent substitution, without departing from the reality of technical solution of the present invention
Matter and scope.
Claims (6)
1. a kind of effective microblogging junk user finds system, it is characterised in that including modeling module, user's sort module and rubbish
Rubbish user's determining module, the modeling module are used to establish microblog users network model, and user's sort module is based on microblogging
User network model is classified to microblog users, and the junk user determining module is based on user's classification and determined in microblog users
Junk user；The microblog users network model is established based on user's concern relation, is specially：User in microblogging is paid close attention to and closed
The digraph H=(W, B) that system is formed is used as microblog users network model, wherein, W is microblog users set, and B is side collection, if
There is concern relation in user, then side be present between user.
2. effective microblogging junk user according to claim 1 finds system, it is characterised in that user's classification mould
Block includes characteristic of division determining unit and taxon, and the characteristic of division determining unit is used to determine the feature for classifying,
The taxon is used to determine class of subscriber according to characteristic of division；The characteristic of division determining unit is classified including junk user
Feature determination subelement and normal users characteristic of division determination subelement, the junk user characteristic of division determination subelement are used for
It is determined that judging the feature of junk user, the normal users characteristic of division determination subelement is used to determine the spy for judging normal users
Sign.
3. effective microblogging junk user according to claim 2 finds system, it is characterised in that the judgement rubbish is used
The feature at family includes the first junk user feature F1With the second junk user feature F2：
The first junk user feature determines in the following ways：Calculate the first junk user characteristic index of user：
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</mrow>
In formula, p (xi) represent the time series of user being divided into m subsequence, xthiIndividual subsequence number of publishing the news accounts for always
The ratio of time series, if wl1≥T1, then the user meet the first junk user feature, T1For given threshold；
The second junk user feature determines in the following ways：Calculate the second junk user characteristic index of user：
<mrow>
<msub>
<mi>wl</mi>
<mn>2</mn>
</msub>
<mo>=</mo>
<msup>
<mi>e</mi>
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<mn>1</mn>
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</msub>
</mrow>
<mi>d</mi>
</mfrac>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
In formula, d1Represent the message number that user includes "@" in publishing the news, l1Represent that user includes in publishing the news
The message number of " http//", d represent the message sum that user delivers；If wl2≥T2, then it represents that user meets the second rubbish
User characteristics, T2For given threshold.
4. effective microblogging junk user according to claim 3 finds system, it is characterised in that the judgement is just conventional
The feature at family includes the first normal users feature C1With the second normal users feature C2：
The first normal users feature determines in the following ways：Calculate the first normal users characteristic index of user：
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</mrow>
<mo>&rsqb;</mo>
</mrow>
</mrow>
If wz1≤T3, then the user meet the first normal users feature, T3For given threshold；
The second normal users feature determines in the following ways：Calculate the second normal users characteristic index of user：
<mrow>
<msub>
<mi>wz</mi>
<mn>2</mn>
</msub>
<mo>=</mo>
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</mrow>
<mi>d</mi>
</mfrac>
</mrow>
If wz2≤T4, then it represents that user meets the second normal users feature, T4For given threshold.
5. effective microblogging junk user according to claim 4 finds system, it is characterised in that the taxon is adopted
Class of subscriber is determined with the following methods：
(1) for any user w ∈ W, the characteristic set F={ F for judging junk user are giveni, i=1,2, if meeting rubbish
The ith feature of user, then it can be high as the probability of junk user, if only existing a feature so that user w has higher
Probability be junk user, then the user is doubtful junk user, if there is two features so that user w has higher general
Rate is junk user, then the user is approximate junk user；
(2) for any user w ∈ W, the characteristic set C={ C for judging normal users are givenj, j=1,2, if met normal
J-th of feature of user, then it can be high as the probability of normal users, if only existing a feature so that user w has higher
Probability be normal users, then the user is doubtful normal users, if there is two features so that user w has higher general
Rate is normal users, then the user is approximate normal users；
(3) for any user w ∈ W, if being both unsatisfactory for junk user judges feature, normal users is also unsatisfactory for and judge spy
Sign, then user w is uncertain user.
6. effective microblogging junk user according to claim 5 finds system, it is characterised in that the junk user is true
Cover half block determines junk user in the following ways：
(1) user's score DF is calculated：
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</msup>
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</mrow>
</msqrt>
</mrow>
In formula, a1Represent the number that user is paid close attention to by doubtful normal users, a2Represent the number that user is paid close attention to by approximate normal users
Mesh, b1Represent that user pays close attention to the number of doubtful junk user, b2Represent that user pays close attention to the number of approximate junk user；
(2) if user is approximate junk user and meets user's score DF>0.2, if user is doubtful junk user and meets to use
Family score DF>0.5, if user is uncertain user and meets user's score DF>1, if user is doubtful normal users and satisfaction
User's score DF>2, if user is approximate normal users and meets user's score DF>4, then user is defined as junk user, it is no
It is then normal users.
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